7) Safety, reliability and interpretability. Safety and reliability are critical to practical applications, e.g. self-driving vehicles. In these scenarios, even a small error of pose or scene estimates will cause disasters to the entire system. Deep neural networks have been long-critisized as 'black-box', exacerbating the safety concerns for critical tasks. Some initial efforts explored the interpretability on deep models [1]}. For example, uncertainty estimation[2]}, [3]} can offer a belief metric, representing to what extent we trust our models. In this way, the unreliable predictions (with low uncertainty) are avoided in order to ensure the systems to stay safe and reliable.
